Figure 7:

Degree can predict edges within a given network but does not generalize to networks with different degree distributions. The edge prior is able to reconstruct the networks on which it was computed (task 1, “unsampled,” 20 different networks) with high performance. When computed on a sampled network, the edge prior can reconstruct the unsampled network with slightly lower performance (task 2, “sampled,” 20 different networks). However, when computed on a completely different network (having a different degree distribution) of the same type of data, the edge prior’s performance is greatly reduced (task 3, “separate,” 3 different networks). The performance reduction from computing predictors on sampled networks is real but far smaller compared to a new degree distribution. This indicates that while degree can be effective for network reconstruction, it is far less effective in predicting edges from a different degree distribution.